Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912825528549376 |
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| author | Lammert, Jacqueline Pfarr, Nicole Kuligin, Leonid Mathes, Sonja Dreyer, Tobias Modersohn, Luise Metzger, Patrick Ferber, Dyke Kather, Jakob Nikolas Truhn, Daniel Adams, Lisa Christine Bressem, Keno Kyrill Lange, Sebastian Schwamborn, Kristina Boeker, Martin Kiechle, Marion Schatz, Ulrich A. Bronger, Holger Tschochohei, Maximilian |
| author_facet | Lammert, Jacqueline Pfarr, Nicole Kuligin, Leonid Mathes, Sonja Dreyer, Tobias Modersohn, Luise Metzger, Patrick Ferber, Dyke Kather, Jakob Nikolas Truhn, Daniel Adams, Lisa Christine Bressem, Keno Kyrill Lange, Sebastian Schwamborn, Kristina Boeker, Martin Kiechle, Marion Schatz, Ulrich A. Bronger, Holger Tschochohei, Maximilian |
| contents | Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for therapy matching. This study explores the use of large language models (LLMs) to construct digital twins for precision medicine in RGTs.
Our proof-of-concept digital twin system integrates clinical and biomarker data from institutional and published cases (n=21) and literature-derived data (n=655 publications with n=404,265 patients) to create tailored treatment plans for metastatic uterine carcinosarcoma, identifying options potentially missed by traditional, single-source analysis. LLM-enabled digital twins efficiently model individual patient trajectories. Shifting to a biology-based rather than organ-based tumor definition enables personalized care that could advance RGT management and thus enhance patient outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors Lammert, Jacqueline Pfarr, Nicole Kuligin, Leonid Mathes, Sonja Dreyer, Tobias Modersohn, Luise Metzger, Patrick Ferber, Dyke Kather, Jakob Nikolas Truhn, Daniel Adams, Lisa Christine Bressem, Keno Kyrill Lange, Sebastian Schwamborn, Kristina Boeker, Martin Kiechle, Marion Schatz, Ulrich A. Bronger, Holger Tschochohei, Maximilian Computation and Language Artificial Intelligence Quantitative Methods Machine Learning Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for therapy matching. This study explores the use of large language models (LLMs) to construct digital twins for precision medicine in RGTs. Our proof-of-concept digital twin system integrates clinical and biomarker data from institutional and published cases (n=21) and literature-derived data (n=655 publications with n=404,265 patients) to create tailored treatment plans for metastatic uterine carcinosarcoma, identifying options potentially missed by traditional, single-source analysis. LLM-enabled digital twins efficiently model individual patient trajectories. Shifting to a biology-based rather than organ-based tumor definition enables personalized care that could advance RGT management and thus enhance patient outcomes. |
| title | Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors |
| topic | Computation and Language Artificial Intelligence Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2409.00544 |